project6-dev
09d92dce5d
fix(build): remove 1106 extra CCCL headers — keep only 288 needed by allocator
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commit 4c365b8c
2026-08-13 13:40:17 +00:00
project6-dev
f6cf9d662e
fix(CCCL): split compilation to isolate CCCL headers from torch/corex
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Two problems from real BI-V100 build:
1. 'CUDA versions below 12 are not supported'
→ Add CCCL_IGNORE_DEPRECATED_CUDA_BELOW_12 (official suppress macro)
2. corex thrust/complex.h conflicts with CCCL thrust headers
→ Split into two compilation units:
- cccl_moe_sort_scatter.cu: CCCL headers only, C API, no torch
- cccl_moe_sort_scatter_pybind.cpp: torch headers only, no CCCL
Same pattern as proven cccl_allocator_preload.cu
3. Variadic device functions rejected by corex clang:
→ is_referenceable.h: __test(...) → __test(long)
→ invoke.h: __any(...) → template __any(_T)
→ conjunction.h: __and_helper(...) → __and_helper(long)
SFINAE still works: int overload wins, long is fallback.
2026-08-13 11:35:43 +00:00
project6-dev
4c365b8c03
feat(CCCL): device-level CUB algorithms for MoE dispatch
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Add complete CCCL CUB header tree (1394 files) to cccl_preload/include/:
- cub/device/ — DeviceRadixSort, DeviceScan, DeviceHistogram, DeviceReduce, DeviceSelect
- cub/agent/ — all agent implementations (sort, scan, reduce, histogram, etc)
- cub/block/ — BlockScan, BlockReduce, BlockExchange, BlockLoad, BlockStore, etc
- cub/warp/ — WarpScan, WarpReduce, WarpExchange, WarpMergeSort
- cub/thread/ — thread-level operators
- thrust/ — sort_by_key, iterator utilities
- cuda/ — execution, stream, memory_resource, functional
New kernel: cccl_moe_sort_scatter.cu
- Uses CUB DeviceRadixSort::SortPairs to sort (expert_id, token_idx) pairs
- O(n) radix sort replaces O(n log n) torch.argsort in MoE prefill path
- Boundary detection + fill for expert offsets/sizes
- Compiled against CCCL upstream headers (not corex CUB) to avoid BI-V100 bugs
Previously only 288 CCCL headers (CachingDeviceAllocator only).
Now 1394 headers — full CUB device-level algorithm stack available for
all future kernels.
2026-08-13 11:18:52 +00:00
Claude
45161610f0
fix: thread_local reentrant guard — prevent cudaMalloc infinite recursion
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CUB CachingDeviceAllocator::DeviceAllocate calls cudaMalloc internally
on cache miss. Without a guard, our intercepted cudaMalloc recurses
into DeviceAllocate → cudaMalloc → DeviceAllocate → segfault.
thread_local g_in_allocator flag detects reentrant calls and forwards
them directly to the real cudaMalloc/cudaFree via dlsym(RTLD_NEXT).
2026-08-13 10:37:32 +00:00
Claude
3ce5bff10f
fix: use cccl_preload::cub namespace — CUB_WRAPPED_NAMESPACE requires it
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CUB_DISABLE_NAMESPACE_MAGIC requires CUB_WRAPPED_NAMESPACE.
CUB_WRAPPED_NAMESPACE=cccl_preload wraps cub into cccl_preload::cub.
Source must use cccl_preload::cub::CachingDeviceAllocator.
2026-08-13 10:36:27 +00:00
Claude
c1e23615b5
fix: remove CUB_WRAPPED_NAMESPACE and _CCCL_COMPILER_GCC from build flags
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CUB_WRAPPED_NAMESPACE=cccl_preload wraps cub into cccl_preload::cub
but cccl_allocator_preload.cu uses bare cub:: — compilation fails.
_CCCL_COMPILER_GCC=1 conflicts with CCCL auto-detection (redefined warning).
Drop both. CUB_DISABLE_NAMESPACE_MAGIC alone is sufficient.
2026-08-13 10:35:11 +00:00
dylanyunlon
a6b5891bfc
feat: CCCL CachingDeviceAllocator preload — 完整依赖链 288 files
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从 cccl_upstream 递归追踪 cub/util_allocator.cuh 的全部 include 依赖:
cub/ 9 files (config, util_*, version, detect_cuda_runtime)
cuda/ libcudacxx type_traits, concepts, algorithm, iterator...
nv/ target macros, preprocessor
总计 288 个头文件 (1.4MB)，打包到 include/ 目录，编译时 -I include
即可完全脱离 CCCL 原始目录结构。
.cu 文件直接 #include <cub/util_allocator.cuh>，
走原版 CUB CachingDeviceAllocator，零 mock。
BI-V100 参数: growth=2 bins=[8..32] max_cached=8GB/device
2026-08-13 09:53:42 +00:00
dylanyunlon
8dc6462a2b
feat: CCCL CachingDeviceAllocator LD_PRELOAD — bypass CoreX expandable_segments ASSERT
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从 CCCL upstream cub/cub/util_allocator.cuh 提取 CachingDeviceAllocator
核心算法，去掉所有 CUB/CCCL 宏依赖，编译为独立 .so。
用 LD_PRELOAD 拦截 cudaMalloc/cudaFree，路由到 CUB 的 geometric-bin
缓存分配器。同时在 constructor 中 strip PYTORCH_CUDA_ALLOC_CONF 里的
expandable_segments 配置，避免 CoreX CUDACachingAllocator.cpp:545 ASSERT。
BI-V100 调优参数:
bin_growth=8, min_bin=3 (512B), max_bin=13 (~550MB)
max_cached_bytes=4GB per device (32GB卡的合理上限)
真机测试步骤:
1. bash build_cccl_preload.sh
2. LD_PRELOAD=./libcccl_allocator.so CCCL_ALLOC_DEBUG=1 \
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
python3 verify_preload.py
2026-08-13 09:53:42 +00:00
dylanyunlon
089e810984
feat: CCCL CachingDeviceAllocator preload — 完整依赖链 288 files
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从 cccl_upstream 递归追踪 cub/util_allocator.cuh 的全部 include 依赖:
cub/ 9 files (config, util_*, version, detect_cuda_runtime)
cuda/ libcudacxx type_traits, concepts, algorithm, iterator...
nv/ target macros, preprocessor
总计 288 个头文件 (1.4MB)，打包到 include/ 目录，编译时 -I include
即可完全脱离 CCCL 原始目录结构。
.cu 文件直接 #include <cub/util_allocator.cuh>，
走原版 CUB CachingDeviceAllocator，零 mock。
BI-V100 参数: growth=2 bins=[8..32] max_cached=8GB/device
2026-08-13 09:53:19 +00:00
dylanyunlon
e7c703ef94
feat: CCCL CachingDeviceAllocator LD_PRELOAD — bypass CoreX expandable_segments ASSERT
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从 CCCL upstream cub/cub/util_allocator.cuh 提取 CachingDeviceAllocator
核心算法，去掉所有 CUB/CCCL 宏依赖，编译为独立 .so。
用 LD_PRELOAD 拦截 cudaMalloc/cudaFree，路由到 CUB 的 geometric-bin
缓存分配器。同时在 constructor 中 strip PYTORCH_CUDA_ALLOC_CONF 里的
expandable_segments 配置，避免 CoreX CUDACachingAllocator.cpp:545 ASSERT。
BI-V100 调优参数:
bin_growth=8, min_bin=3 (512B), max_bin=13 (~550MB)
max_cached_bytes=4GB per device (32GB卡的合理上限)
真机测试步骤:
1. bash build_cccl_preload.sh
2. LD_PRELOAD=./libcccl_allocator.so CCCL_ALLOC_DEBUG=1 \
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
python3 verify_preload.py
2026-08-13 09:53:19 +00:00
dylanyunlon
ddfd24da27
fix: sync to real-machine verified version — ALL TESTS PASSED
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真机验证通过的精确版本:
- CUB_NS_QUALIFIER (不是 cub::)
- thread_local inside_cub reentrant guard
- 去掉 -D_CCCL_COMPILER_GCC=1
- total_mem → total_memory
BI-V100 32GB × Iluvatar, CoreX clang++ 编译 51864 bytes .so
expandable_segments:True 被 strip, CUB allocator 接管, 缓存复用确认
2026-08-13 09:52:15 +00:00
dylanyunlon
93e498197a
fix: thread_local reentrant guard — prevent cudaMalloc infinite recursion
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CUB CachingDeviceAllocator 内部在 cache miss 时调 cudaMalloc，
被我们的 LD_PRELOAD 再次拦截 → DeviceAllocate → cudaMalloc → 无限递归 → segfault。
加 thread_local bool inside_cub 标志:
外部调用 → CUB allocator (带缓存)
CUB 内部调用 → 直接走 dlsym(RTLD_NEXT) 的真实 cudaMalloc
2026-08-13 09:42:26 +00:00
dylanyunlon
0ac118911d
fix: CUB_NS_QUALIFIER for wrapped namespace + drop _CCCL_COMPILER_GCC
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CoreX clang++ 不是 GCC，-D_CCCL_COMPILER_GCC=1 和 CCCL 自己的
compiler detection 冲突。
CUB_WRAPPED_NAMESPACE=cccl_preload 使得命名空间变成 cccl_preload::cub，
用 CUB_NS_QUALIFIER 宏自动解析正确的命名空间。
2026-08-13 09:31:45 +00:00
dylanyunlon
8d6f9eaeb0
feat: CCCL CachingDeviceAllocator preload — 完整依赖链 288 files
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从 cccl_upstream 递归追踪 cub/util_allocator.cuh 的全部 include 依赖:
cub/ 9 files (config, util_*, version, detect_cuda_runtime)
cuda/ libcudacxx type_traits, concepts, algorithm, iterator...
nv/ target macros, preprocessor
总计 288 个头文件 (1.4MB)，打包到 include/ 目录，编译时 -I include
即可完全脱离 CCCL 原始目录结构。
.cu 文件直接 #include <cub/util_allocator.cuh>，
走原版 CUB CachingDeviceAllocator，零 mock。
BI-V100 参数: growth=2 bins=[8..32] max_cached=8GB/device
2026-08-13 09:26:41 +00:00
dylanyunlon
967d572073
feat: CCCL CachingDeviceAllocator LD_PRELOAD — bypass CoreX expandable_segments ASSERT
...
从 CCCL upstream cub/cub/util_allocator.cuh 提取 CachingDeviceAllocator
核心算法，去掉所有 CUB/CCCL 宏依赖，编译为独立 .so。
用 LD_PRELOAD 拦截 cudaMalloc/cudaFree，路由到 CUB 的 geometric-bin
缓存分配器。同时在 constructor 中 strip PYTORCH_CUDA_ALLOC_CONF 里的
expandable_segments 配置，避免 CoreX CUDACachingAllocator.cpp:545 ASSERT。
BI-V100 调优参数:
bin_growth=8, min_bin=3 (512B), max_bin=13 (~550MB)
max_cached_bytes=4GB per device (32GB卡的合理上限)
真机测试步骤:
1. bash build_cccl_preload.sh
2. LD_PRELOAD=./libcccl_allocator.so CCCL_ALLOC_DEBUG=1 \
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
python3 verify_preload.py
2026-08-13 09:26:41 +00:00
